plot(): analyze distributions

Overview

The function plot() explores the distributions and statistics of the dataset. It generates a variety of visualizations and statistics which enables the user to achieve a comprehensive understanding of the column distributions and their relationships. The following describes the functionality of plot() for a given dataframe df.

1. plot(df): plots the distribution of each column and computes dataset statistics

2. plot(df, x): plots the distribution of column x in various ways, and computes its statistics

3. plot(df, x, y): generates plots depicting the relationship between columns x and y

The generated plots are different for numerical and categorical columns. The following table summarizes the output for the different column types.

x	y	Output
None	None	dataset statistics, histogram or bar chart for each column
Numerical	None	column statistics, histogram, kde plot, qq-normal plot, box plot
Categorical	None	column statistics, bar chart, pie chart, word cloud, word frequencies
Numerical	Numerical	scatter plot, hexbin plot, binned box plot
Numerical	Categorical	categorical box plot, multi-line chart
Categorical	Numerical	categorical box plot, multi-line chart
Categorical	Categorical	nested bar chart, stacked bar chart, heat map

Next, we demonstrate the functionality of plot().

Load the dataset

EDAx supports Pandas and Dask dataframes. Here, we will load the well-known adult dataset into a Pandas dataframe.

import pandas as pd
df = pd.read_csv("https://www.openml.org/data/get_csv/1595261/phpMawTba", na_values = [' ?'])

Get an overview of the dataset with plot(df)

We start by calling plot(df) which computes dataset-level statistics, a histogram for each numerical column, and a bar chart for each categorical column. The number of bins in the histogram can be specified with the parameter bins, and the number of categories in the bar chart can be specified with the parameter ngroups. If a column contains missing values, the percent of missing values is shown in the title and ignored when generating the plots.

from edax import plot
plot(df)

Stats and Insights

Dataset Statistics

Number of Variables	15
Number of Rows	48842
Missing Cells	6465
Missing Cells (%)	0.9%
Duplicate Rows	52
Duplicate Rows (%)	0.1%
Total Size in Memory	30.1 MB
Average Row Size in Memory	645.7 B
Variable Types	Numerical: 6 Categorical: 9

Dataset Insights

workclass has 2799 (5.73%) missing values	Missing
occupation has 2809 (5.75%) missing values	Missing
native-country has 857 (1.75%) missing values	Missing
fnlwgt is skewed	Skewed
education-num is skewed	Skewed
capital-gain is skewed	Skewed
capital-loss is skewed	Skewed
hours-per-week is skewed	Skewed
capital-gain has 44807 (91.74%) zeros	Zeros
capital-loss has 46560 (95.33%) zeros	Zeros

age

workclass

Column Insights

1. workclass has 2799 (5.73%) missing values

fnlwgt

Column Insights

1. fnlwgt is skewed

education

education-num

Column Insights

1. education-num is skewed

marital-status

occupation

Column Insights

1. occupation has 2809 (5.75%) missing values

relationship

race

sex

capital-gain

Column Insights

1. capital-gain is skewed
2. capital-gain has 44807 (91.74%) zeros

capital-loss

Column Insights

1. capital-loss is skewed
2. capital-loss has 46560 (95.33%) zeros

hours-per-week

Column Insights

1. hours-per-week is skewed

native-country

Column Insights

1. native-country has 857 (1.75%) missing values

class

Understand a column with plot(df, x)

After getting an overview of the dataset, we can thoroughly investigate a column of interest x using plot(df, x). The output is of plot(df, x) is different for numerical and categorical columns.

When x is a numerical column, it computes column statistics, and generates a histogram, kde plot, box plot and qq-normal plot:

plot(df, "age")

Stats Histogram KDE Plot Normal Q-Q Plot Box Plot

Overview

Distinct Count	74
Unique (%)	0.2%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Memory Size	763.2 KB
Mean	38.6436
Minimum	17
Maximum	90
Zeros	0
Zeros (%)	0.0%
Negatives	0
Negatives (%)	0.0%

Quantile Statistics

Minimum	17
5-th Percentile	19
Q1	28
Median	37
Q3	48
95-th Percentile	63
Maximum	90
Range	73
IQR	20

Descriptive Statistics

Mean	38.6436
Standard Deviation	13.7105
Variance	187.9781
Sum	1.8874e+06
Skewness	0.5576
Kurtosis	-0.1844
Coefficient of Variation	0.3548

1. age has 216 outliers

When x is a categorical column, it computes column statistics, and plots a bar chart and pie chart:

plot(df, "education")

Stats Bar Chart Pie Chart Word Cloud Word Frequency Word Length

Overview

Distinct Count	16
Unique (%)	0.0%
Missing	0
Missing (%)	0.0%
Memory Size	3.5 MB

Length

Mean	9.4221
Standard Deviation	2.4401
Median	8
Minimum	4
Maximum	13

Sample

1st row	11th
2nd row	HS-grad
3rd row	Assoc-acdm
4th row	Some-college
5th row	Some-college

Letter

Count	366588
Lowercase Letter	308287
Space Separator	48842
Uppercase Letter	58301
Dash Punctuation	32869
Decimal Number	11894

1. The top 2 categories ( HS-grad, Some-college) take over 50%

1. education contains many words: 48842 words

Understand the relationship between two columns with plot(df, x, y)

Next, we can explore the relationship between columns x and y using plot(df, x, y). The output depends on the types of the columns.

When x and y are both numerical columns, it generates a scatter plot, hexbin plot and box plot:

plot(df, "age", "hours-per-week")

When x and y are both categorical columns, it plots a nested bar chart, stacked bar chart and heat map:

plot(df, "education", "marital-status")

When x and y are one each of type numerical and categorical, it generates a box plot per category and a multi-line chart:

plot(df, "age", "education")
# or plot(df, "education", "age")